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Copy file name to clipboardExpand all lines: DEPLOYMENT.md
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This is the most practical default. The repo already includes Redis, the Manim runtime, and the Node runtime in the deployment path.
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If you have already published the image, you can also deploy from `wingflow/manimcat` instead of rebuilding locally each time.
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### 1. Prepare environment variables
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```bash
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docker compose up -d
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```
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If you want to use the published image directly, replace the `build` section in `docker-compose.yml` with:
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```yaml
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image: wingflow/manimcat
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```
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### 3. Verify
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```bash
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Do not follow older instructions that mention `Dockerfile.huggingface`. That file is not part of the current repo.
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If you have already published a Docker image, other environments can reference `wingflow/manimcat`; however, Hugging Face Spaces still builds from the repository `Dockerfile` rather than running a Docker Hub image directly.
Copy file name to clipboardExpand all lines: README.md
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I am happy to introduce my new project, ManimCat. It is, after all, a cat.
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Built on top of [manim-video-generator](https://github.com/rohitg00/manim-video-generator), ManimCat is now a much broader AI-assisted creation system for math teaching visuals rather than just a single generation flow.
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It is designed for classroom explanation, worked-example breakdowns, and visual reasoning tasks. You can use natural language to generate, modify, rerender, and organize Manim-based teaching content with both `video` and `image` outputs.
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The project currently has two distinct modes: `Workflow Mode` for direct generation and rendering, and `Agent Mode` for Studio-based collaborative work with longer-lived sessions, review, and iteration.
Built on top of [manim-video-generator](https://github.com/rohitg00/manim-video-generator), ManimCat is now a much broader AI-assisted creation system for math teaching visuals rather than just a single generation flow.
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It is designed for classroom explanation, worked-example breakdowns, and visual reasoning tasks. You can use natural language to generate, modify, rerender, and organize Manim-based teaching content with both `video` and `image` outputs.
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The project currently has two distinct modes: `Workflow Mode` for direct generation and rendering, and `Agent Mode` for Studio-based collaborative work with longer-lived sessions, review, and iteration.
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-`Workflow Mode` currently supports two direct output paths: `video` and `image`
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-`Agent Mode` currently includes two studios: `Plot Studio` (about 60% complete) and `Manim Studio` (about 20% complete)
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-`Plot Studio` is for matplotlib-based static math visuals, charts, and teaching figures
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-`Manim Studio` is for Manim animation workflows, but it is still at an early stage
<imgsrc="https://github.com/user-attachments/assets/0812b601-b896-4137-8e20-a2d4b6feadb9"width="46%"alt="ManimCat Plot Studio screenshot 1" />
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<imgsrc="https://github.com/user-attachments/assets/99ae423f-4b15-431d-8e21-30a6b6171616"width="46%"alt="ManimCat Plot Studio screenshot 2" />
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</div>
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## Examples
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npm run dev
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```
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Open `http://localhost:3000`. For environment variables and deployment-specific setup, see the [deployment guide](https://github.com/Wing900/ManimCat/blob/main/DEPLOYMENT.md).
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Open `http://localhost:3000`. For environment variables and deployment-specific setup, see the [deployment guide](https://github.com/Wing900/ManimCat/blob/main/DEPLOYMENT.md).
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If you want a direct Docker deployment path, you can also start from the published image `wingflow/manimcat` instead of building locally first.
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